TY - GEN
T1 - An efficient distributed range query processing algorithm on LiDAR data
AU - Chung, Yu Chi
AU - Su, I. Fang
AU - Lee, Chiang
AU - Gu, Gary
N1 - Funding Information:
ACKNOWLEDGMENT This work is supported by ministry of science and technology (R.O.C.) under Grants MOST 105-2119-M-309-001 - and MOST 105-2221-E-006-218 -
Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/18
Y1 - 2017/10/18
N2 - With the rapid development of the LiDAR (Light Detection and Ranging) remote sensing technology in the past decade, LiDAR sensing systems have become an important source for acquiring environmental data. The LiDAR system can be equipped on an aircraft to collect geographic information in a wide area. One characteristic of the LiDAR system is that it usually produces huge volumes of data. Thus, how to efficiently manage, store, process and visualize the LiDAR data sources has become an important and challenging research issue in the spatial database community. In this paper, we propose a distributed algorithm to process a remarkable spatial query (i.e., range queries) over massive LiDAR data points. Different from existing range query processing approaches which assume all data points are stored in a centralized server, our method adopts a decentralized fashion. Our query processing system is a master/slave architecture. A large data set is split into smaller partitions that are distributed among several slave machines. Therefore, each slave machine only process a small part of data points. We also develop index structures over LiDAR data sets to further enhance the efficiency of query processing. Our performance study proves the efficiency of the design.
AB - With the rapid development of the LiDAR (Light Detection and Ranging) remote sensing technology in the past decade, LiDAR sensing systems have become an important source for acquiring environmental data. The LiDAR system can be equipped on an aircraft to collect geographic information in a wide area. One characteristic of the LiDAR system is that it usually produces huge volumes of data. Thus, how to efficiently manage, store, process and visualize the LiDAR data sources has become an important and challenging research issue in the spatial database community. In this paper, we propose a distributed algorithm to process a remarkable spatial query (i.e., range queries) over massive LiDAR data points. Different from existing range query processing approaches which assume all data points are stored in a centralized server, our method adopts a decentralized fashion. Our query processing system is a master/slave architecture. A large data set is split into smaller partitions that are distributed among several slave machines. Therefore, each slave machine only process a small part of data points. We also develop index structures over LiDAR data sets to further enhance the efficiency of query processing. Our performance study proves the efficiency of the design.
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U2 - 10.1109/UMEDIA.2017.8074115
DO - 10.1109/UMEDIA.2017.8074115
M3 - Conference contribution
AN - SCOPUS:85039896683
T3 - Ubi-Media 2017 - Proceedings of the 10th International Conference on Ubi-Media Computing and Workshops with the 4th International Workshop on Advanced E-Learning and the 1st International Workshop on Multimedia and IoT: Networks, Systems and Applications
BT - Ubi-Media 2017 - Proceedings of the 10th International Conference on Ubi-Media Computing and Workshops with the 4th International Workshop on Advanced E-Learning and the 1st International Workshop on Multimedia and IoT
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Ubi-Media Computing and Workshops, Ubi-Media 2017
Y2 - 1 August 2017 through 4 August 2017
ER -